Human disease is represented and analyzed in terms of incompletely observed stochastic phenomena, making use of a variety of models, data structures and computer-implemented analytic procedures. Mathematico-statistical computer models are being exploited for prediction and explanation with respect to the natural history of disease, with the potential to elucidate the effect of treatment on the course of disease. The resulting class of problems are often NP-hard, when considered in terms of computational complexity. No polynomial-time procedure is known for the optimal solution of these problems. Attention is focused on heuristically motivated, computationally feasible approximate solutions. Techniques developed and used include methods for statistical treatment to mitigate the effect of missing and incomplete data. Covariance structure and path analytic models for statistical analyses of normally distributed data will continue to be studied. Utilizing the grade of membership (GOM) model, an analysis will be made on diseases with multiple causes; in particular, separate analyses of pneumonia and urinary tract infections. The Bayesian approach to diagnosis of patients with a single disease is being extended to an empirical Bayes approach to multiply-diseased patients. Additional studies, such as the finite mixture approach to latent disease, will be performed.